Overview

Dataset statistics

Number of variables25
Number of observations7395
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory200.0 B

Variable types

Categorical2
Numeric23

Alerts

section has a high cardinality: 2465 distinct values High cardinality
total is highly correlated with single_women_aged_16_to_64 and 21 other fieldsHigh correlation
single_women_aged_16_to_64 is highly correlated with total and 21 other fieldsHigh correlation
single_men_aged_16_to_64 is highly correlated with total and 21 other fieldsHigh correlation
single_women_aged_65_or_over is highly correlated with total and 21 other fieldsHigh correlation
single_men_aged_65_or_over is highly correlated with total and 21 other fieldsHigh correlation
adult_women_with_one_or_more_minors is highly correlated with total and 21 other fieldsHigh correlation
two_adults_from_16_to_64_and_without_minors is highly correlated with total and 21 other fieldsHigh correlation
two_adults_one_at_least_65_and_without_minors is highly correlated with total and 21 other fieldsHigh correlation
two_adults_and_one_minor is highly correlated with total and 21 other fieldsHigh correlation
two_adults_and_two_minors is highly correlated with total and 21 other fieldsHigh correlation
two_adults_and_three_or_more_minors is highly correlated with total and 21 other fieldsHigh correlation
two_adults_over_35_and_one_adult_from_16_to_34 is highly correlated with total and 21 other fieldsHigh correlation
two_adults_over_35_and_one_adult_from_16_to_34_and_one_minor is highly correlated with total and 21 other fieldsHigh correlation
three_adults_and_0_or_more_minors is highly correlated with total and 21 other fieldsHigh correlation
two_adults_over_35_and_two_adults_from_16_to_34 is highly correlated with total and 21 other fieldsHigh correlation
four_adults_and_0_or_more_minors is highly correlated with total and 21 other fieldsHigh correlation
five_adults_and_0_or_more_minors is highly correlated with total and 21 other fieldsHigh correlation
adult_men_with_one_or_more_minors is highly correlated with total and 21 other fieldsHigh correlation
two_adults_over_35_and_one_adult_from_16_to_34_and_two_minors is highly correlated with total and 21 other fieldsHigh correlation
two_adults_over_35_and_two_adults_from_16_to_34_and_one_minor is highly correlated with total and 21 other fieldsHigh correlation
two_adults_over_35_and_two_adults_from_16_to_34_and_two_or_more_minors is highly correlated with total and 21 other fieldsHigh correlation
fifteen_or_more_inhabitants is highly correlated with total and 21 other fieldsHigh correlation
only_minors is highly correlated with total and 21 other fieldsHigh correlation
total is highly skewed (γ1 = 46.1229873) Skewed
single_women_aged_16_to_64 is highly skewed (γ1 = 45.86347887) Skewed
single_men_aged_16_to_64 is highly skewed (γ1 = 45.86109416) Skewed
single_women_aged_65_or_over is highly skewed (γ1 = 45.94078563) Skewed
single_men_aged_65_or_over is highly skewed (γ1 = 46.02218853) Skewed
adult_women_with_one_or_more_minors is highly skewed (γ1 = 45.9861885) Skewed
adult_men_with_one_or_more_minors is highly skewed (γ1 = 45.81043154) Skewed
two_adults_from_16_to_64_and_without_minors is highly skewed (γ1 = 46.09072256) Skewed
two_adults_one_at_least_65_and_without_minors is highly skewed (γ1 = 45.9380385) Skewed
two_adults_and_one_minor is highly skewed (γ1 = 46.03156901) Skewed
two_adults_and_two_minors is highly skewed (γ1 = 45.85469424) Skewed
two_adults_and_three_or_more_minors is highly skewed (γ1 = 45.54969666) Skewed
two_adults_over_35_and_one_adult_from_16_to_34 is highly skewed (γ1 = 45.98573939) Skewed
two_adults_over_35_and_one_adult_from_16_to_34_and_one_minor is highly skewed (γ1 = 45.83097138) Skewed
two_adults_over_35_and_one_adult_from_16_to_34_and_two_minors is highly skewed (γ1 = 45.93022907) Skewed
three_adults_and_0_or_more_minors is highly skewed (γ1 = 46.08926491) Skewed
two_adults_over_35_and_two_adults_from_16_to_34 is highly skewed (γ1 = 46.03605095) Skewed
two_adults_over_35_and_two_adults_from_16_to_34_and_one_minor is highly skewed (γ1 = 45.95178104) Skewed
two_adults_over_35_and_two_adults_from_16_to_34_and_two_or_more_minors is highly skewed (γ1 = 45.53116149) Skewed
four_adults_and_0_or_more_minors is highly skewed (γ1 = 46.0736974) Skewed
five_adults_and_0_or_more_minors is highly skewed (γ1 = 46.19403382) Skewed
fifteen_or_more_inhabitants is highly skewed (γ1 = 45.83761583) Skewed
only_minors is highly skewed (γ1 = 46.13931176) Skewed
section is uniformly distributed Uniform
Year is uniformly distributed Uniform
adult_men_with_one_or_more_minors has 1648 (22.3%) zeros Zeros
two_adults_and_three_or_more_minors has 327 (4.4%) zeros Zeros
two_adults_over_35_and_one_adult_from_16_to_34_and_two_minors has 590 (8.0%) zeros Zeros
two_adults_over_35_and_two_adults_from_16_to_34_and_one_minor has 637 (8.6%) zeros Zeros
two_adults_over_35_and_two_adults_from_16_to_34_and_two_or_more_minors has 2488 (33.6%) zeros Zeros
fifteen_or_more_inhabitants has 6141 (83.0%) zeros Zeros
only_minors has 6310 (85.3%) zeros Zeros

Reproduction

Analysis started2022-10-25 20:42:12.561508
Analysis finished2022-10-25 20:44:17.715152
Duration2 minutes and 5.15 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

section
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2465
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
Ciudad de Madrid
 
3
13143.0
 
3
13135.0
 
3
13136.0
 
3
13137.0
 
3
Other values (2460)
7380 

Length

Max length23
Median length7
Mean length6.64137931
Min length6

Characters and Unicode

Total characters49113
Distinct characters48
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCiudad de Madrid
2nd row01. Centro
3rd row1001.0
4th row1002.0
5th row1003.0

Common Values

ValueCountFrequency (%)
Ciudad de Madrid3
 
< 0.1%
13143.03
 
< 0.1%
13135.03
 
< 0.1%
13136.03
 
< 0.1%
13137.03
 
< 0.1%
13138.03
 
< 0.1%
13139.03
 
< 0.1%
13140.03
 
< 0.1%
13142.03
 
< 0.1%
13144.03
 
< 0.1%
Other values (2455)7365
99.6%

Length

2022-10-25T22:44:17.907295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de9
 
0.1%
vallecas6
 
0.1%
ciudad6
 
0.1%
8035.03
 
< 0.1%
1003.03
 
< 0.1%
1038.03
 
< 0.1%
1022.03
 
< 0.1%
1021.03
 
< 0.1%
1020.03
 
< 0.1%
1019.03
 
< 0.1%
Other values (2481)7443
99.4%

Most occurring characters

ValueCountFrequency (%)
015216
31.0%
18460
17.2%
.7392
15.1%
22781
 
5.7%
32322
 
4.7%
52205
 
4.5%
62100
 
4.3%
42088
 
4.3%
82082
 
4.2%
72013
 
4.1%
Other values (38)2454
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40947
83.4%
Other Punctuation7392
 
15.1%
Lowercase Letter582
 
1.2%
Uppercase Letter93
 
0.2%
Space Separator90
 
0.2%
Dash Punctuation9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a135
23.2%
l63
10.8%
e60
10.3%
r51
 
8.8%
n39
 
6.7%
d33
 
5.7%
i30
 
5.2%
c24
 
4.1%
o24
 
4.1%
t24
 
4.1%
Other values (11)99
17.0%
Uppercase Letter
ValueCountFrequency (%)
C21
22.6%
V15
16.1%
M9
9.7%
L6
 
6.5%
S6
 
6.5%
A6
 
6.5%
B6
 
6.5%
P6
 
6.5%
H3
 
3.2%
U3
 
3.2%
Other values (4)12
12.9%
Decimal Number
ValueCountFrequency (%)
015216
37.2%
18460
20.7%
22781
 
6.8%
32322
 
5.7%
52205
 
5.4%
62100
 
5.1%
42088
 
5.1%
82082
 
5.1%
72013
 
4.9%
91680
 
4.1%
Other Punctuation
ValueCountFrequency (%)
.7392
100.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common48438
98.6%
Latin675
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a135
20.0%
l63
 
9.3%
e60
 
8.9%
r51
 
7.6%
n39
 
5.8%
d33
 
4.9%
i30
 
4.4%
c24
 
3.6%
o24
 
3.6%
t24
 
3.6%
Other values (25)192
28.4%
Common
ValueCountFrequency (%)
015216
31.4%
18460
17.5%
.7392
15.3%
22781
 
5.7%
32322
 
4.8%
52205
 
4.6%
62100
 
4.3%
42088
 
4.3%
82082
 
4.3%
72013
 
4.2%
Other values (3)1779
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII49101
> 99.9%
None12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015216
31.0%
18460
17.2%
.7392
15.1%
22781
 
5.7%
32322
 
4.7%
52205
 
4.5%
62100
 
4.3%
42088
 
4.3%
82082
 
4.2%
72013
 
4.1%
Other values (36)2442
 
5.0%
None
ValueCountFrequency (%)
í6
50.0%
á6
50.0%

total
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct882
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1572.455984
Minimum71
Maximum1307682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:18.330227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile308
Q1405
median500
Q3629
95-th percentile866
Maximum1307682
Range1307611
Interquartile range (IQR)224

Descriptive statistics

Standard deviation26669.43567
Coefficient of variation (CV)16.96037024
Kurtosis2224.531043
Mean1572.455984
Median Absolute Deviation (MAD)109
Skewness46.1229873
Sum11628312
Variance711258798.9
MonotonicityNot monotonic
2022-10-25T22:44:18.567584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49131
 
0.4%
46530
 
0.4%
43929
 
0.4%
43329
 
0.4%
50027
 
0.4%
41226
 
0.4%
38125
 
0.3%
53025
 
0.3%
47225
 
0.3%
51925
 
0.3%
Other values (872)7123
96.3%
ValueCountFrequency (%)
712
< 0.1%
771
< 0.1%
931
< 0.1%
961
< 0.1%
991
< 0.1%
1631
< 0.1%
1661
< 0.1%
1701
< 0.1%
2151
< 0.1%
2191
< 0.1%
ValueCountFrequency (%)
13076821
< 0.1%
12901641
< 0.1%
12782581
< 0.1%
969231
< 0.1%
958901
< 0.1%
956801
< 0.1%
952171
< 0.1%
947801
< 0.1%
946001
< 0.1%
916501
< 0.1%

single_women_aged_16_to_64
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct249
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.9505071
Minimum1
Maximum117998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:18.773649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q125
median40
Q364
95-th percentile117
Maximum117998
Range117997
Interquartile range (IQR)39

Descriptive statistics

Standard deviation2429.404382
Coefficient of variation (CV)16.99472378
Kurtosis2205.762632
Mean142.9505071
Median Absolute Deviation (MAD)17
Skewness45.86347887
Sum1057119
Variance5902005.653
MonotonicityNot monotonic
2022-10-25T22:44:19.000122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28164
 
2.2%
22145
 
2.0%
25144
 
1.9%
18142
 
1.9%
29139
 
1.9%
23137
 
1.9%
31137
 
1.9%
21136
 
1.8%
34134
 
1.8%
27133
 
1.8%
Other values (239)5984
80.9%
ValueCountFrequency (%)
12
 
< 0.1%
23
 
< 0.1%
36
 
0.1%
45
 
0.1%
511
 
0.1%
629
0.4%
728
0.4%
840
0.5%
939
0.5%
1049
0.7%
ValueCountFrequency (%)
1179981
< 0.1%
1174541
< 0.1%
1169211
< 0.1%
116952
< 0.1%
116271
< 0.1%
84541
< 0.1%
83951
< 0.1%
83791
< 0.1%
82981
< 0.1%
82661
< 0.1%

single_men_aged_16_to_64
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct272
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.9168357
Minimum3
Maximum116863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:19.219351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q126
median39
Q360
95-th percentile122
Maximum116863
Range116860
Interquartile range (IQR)34

Descriptive statistics

Standard deviation2395.132228
Coefficient of variation (CV)16.99677839
Kurtosis2205.314988
Mean140.9168357
Median Absolute Deviation (MAD)15
Skewness45.86109416
Sum1042080
Variance5736658.391
MonotonicityNot monotonic
2022-10-25T22:44:19.457732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30184
 
2.5%
29164
 
2.2%
27160
 
2.2%
28160
 
2.2%
31152
 
2.1%
34151
 
2.0%
35148
 
2.0%
32140
 
1.9%
26140
 
1.9%
25136
 
1.8%
Other values (262)5860
79.2%
ValueCountFrequency (%)
35
 
0.1%
42
 
< 0.1%
516
 
0.2%
619
 
0.3%
727
 
0.4%
849
0.7%
947
0.6%
1053
0.7%
1158
0.8%
1274
1.0%
ValueCountFrequency (%)
1168631
< 0.1%
1156911
< 0.1%
1148061
< 0.1%
135961
< 0.1%
133521
< 0.1%
132101
< 0.1%
77531
< 0.1%
76001
< 0.1%
75631
< 0.1%
74091
< 0.1%

single_women_aged_65_or_over
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct200
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.1659229
Minimum1
Maximum126695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:19.701570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q138
median51
Q366
95-th percentile92
Maximum126695
Range126694
Interquartile range (IQR)28

Descriptive statistics

Standard deviation2601.433778
Coefficient of variation (CV)16.98441617
Kurtosis2211.395064
Mean153.1659229
Median Absolute Deviation (MAD)14
Skewness45.94078563
Sum1132662
Variance6767457.702
MonotonicityNot monotonic
2022-10-25T22:44:19.945073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46162
 
2.2%
47154
 
2.1%
41152
 
2.1%
52146
 
2.0%
48146
 
2.0%
54145
 
2.0%
43145
 
2.0%
40144
 
1.9%
50143
 
1.9%
51142
 
1.9%
Other values (190)5916
80.0%
ValueCountFrequency (%)
13
 
< 0.1%
26
 
0.1%
312
 
0.2%
415
 
0.2%
514
 
0.2%
624
0.3%
719
0.3%
824
0.3%
938
0.5%
1044
0.6%
ValueCountFrequency (%)
1266951
< 0.1%
1260441
< 0.1%
1248151
< 0.1%
111731
< 0.1%
110331
< 0.1%
109181
< 0.1%
103971
< 0.1%
103651
< 0.1%
102371
< 0.1%
98341
< 0.1%

single_men_aged_65_or_over
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct113
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.71440162
Minimum0
Maximum37538
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:20.263271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median14
Q319
95-th percentile28
Maximum37538
Range37538
Interquartile range (IQR)8

Descriptive statistics

Standard deviation759.1604838
Coefficient of variation (CV)16.97798598
Kurtosis2218.283867
Mean44.71440162
Median Absolute Deviation (MAD)4
Skewness46.02218853
Sum330663
Variance576324.6401
MonotonicityNot monotonic
2022-10-25T22:44:20.472371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14527
 
7.1%
13461
 
6.2%
12453
 
6.1%
11452
 
6.1%
16443
 
6.0%
15422
 
5.7%
17403
 
5.4%
10401
 
5.4%
18394
 
5.3%
19322
 
4.4%
Other values (103)3117
42.2%
ValueCountFrequency (%)
07
 
0.1%
122
 
0.3%
242
 
0.6%
391
 
1.2%
4129
1.7%
5136
1.8%
6183
2.5%
7259
3.5%
8263
3.6%
9300
4.1%
ValueCountFrequency (%)
375381
< 0.1%
367201
< 0.1%
359631
< 0.1%
31701
< 0.1%
31281
< 0.1%
30851
< 0.1%
28011
< 0.1%
27901
< 0.1%
27411
< 0.1%
27251
< 0.1%

adult_women_with_one_or_more_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct147
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.80446247
Minimum0
Maximum27460
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:20.683588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median9
Q313
95-th percentile32
Maximum27460
Range27460
Interquartile range (IQR)7

Descriptive statistics

Standard deviation557.1725489
Coefficient of variation (CV)16.98465717
Kurtosis2215.776902
Mean32.80446247
Median Absolute Deviation (MAD)3
Skewness45.9861885
Sum242589
Variance310441.2493
MonotonicityNot monotonic
2022-10-25T22:44:20.861774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6649
 
8.8%
8646
 
8.7%
7628
 
8.5%
9574
 
7.8%
5541
 
7.3%
10488
 
6.6%
4487
 
6.6%
11404
 
5.5%
12370
 
5.0%
3337
 
4.6%
Other values (137)2271
30.7%
ValueCountFrequency (%)
018
 
0.2%
179
 
1.1%
2201
 
2.7%
3337
4.6%
4487
6.6%
5541
7.3%
6649
8.8%
7628
8.5%
8646
8.7%
9574
7.8%
ValueCountFrequency (%)
274601
< 0.1%
271711
< 0.1%
262321
< 0.1%
24941
< 0.1%
24901
< 0.1%
24751
< 0.1%
21951
< 0.1%
21571
< 0.1%
20731
< 0.1%
20631
< 0.1%

adult_men_with_one_or_more_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct92
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.615415822
Minimum0
Maximum5491
Zeros1648
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:21.065029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile8
Maximum5491
Range5491
Interquartile range (IQR)2

Descriptive statistics

Standard deviation112.5104547
Coefficient of variation (CV)17.00731409
Kurtosis2201.868089
Mean6.615415822
Median Absolute Deviation (MAD)1
Skewness45.81043154
Sum48921
Variance12658.60241
MonotonicityNot monotonic
2022-10-25T22:44:21.277841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12122
28.7%
01648
22.3%
21482
20.0%
3821
 
11.1%
4452
 
6.1%
5229
 
3.1%
6135
 
1.8%
788
 
1.2%
1057
 
0.8%
956
 
0.8%
Other values (82)305
 
4.1%
ValueCountFrequency (%)
01648
22.3%
12122
28.7%
21482
20.0%
3821
 
11.1%
4452
 
6.1%
5229
 
3.1%
6135
 
1.8%
788
 
1.2%
849
 
0.7%
956
 
0.8%
ValueCountFrequency (%)
54911
< 0.1%
54881
< 0.1%
53281
< 0.1%
6751
< 0.1%
6721
< 0.1%
6481
< 0.1%
5051
< 0.1%
4971
< 0.1%
4931
< 0.1%
3471
< 0.1%

two_adults_from_16_to_64_and_without_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct279
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.8288032
Minimum3
Maximum163183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:21.468034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile25
Q142
median59
Q382
95-th percentile134
Maximum163183
Range163180
Interquartile range (IQR)40

Descriptive statistics

Standard deviation3289.116372
Coefficient of variation (CV)16.96918268
Kurtosis2223.319867
Mean193.8288032
Median Absolute Deviation (MAD)19
Skewness46.09072256
Sum1433364
Variance10818286.51
MonotonicityNot monotonic
2022-10-25T22:44:21.748395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49131
 
1.8%
46124
 
1.7%
47122
 
1.6%
48121
 
1.6%
45118
 
1.6%
44115
 
1.6%
60114
 
1.5%
57113
 
1.5%
61109
 
1.5%
56108
 
1.5%
Other values (269)6220
84.1%
ValueCountFrequency (%)
31
 
< 0.1%
63
 
< 0.1%
71
 
< 0.1%
84
 
0.1%
93
 
< 0.1%
103
 
< 0.1%
1113
0.2%
1214
0.2%
1316
0.2%
1419
0.3%
ValueCountFrequency (%)
1631831
< 0.1%
1584871
< 0.1%
1561181
< 0.1%
135631
< 0.1%
126981
< 0.1%
122771
< 0.1%
112621
< 0.1%
112181
< 0.1%
110641
< 0.1%
110371
< 0.1%

two_adults_one_at_least_65_and_without_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct248
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.4438134
Minimum1
Maximum175581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:21.973695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q151
median68
Q389
95-th percentile128
Maximum175581
Range175580
Interquartile range (IQR)38

Descriptive statistics

Standard deviation3608.353745
Coefficient of variation (CV)16.98497917
Kurtosis2210.878224
Mean212.4438134
Median Absolute Deviation (MAD)19
Skewness45.9380385
Sum1571022
Variance13020216.75
MonotonicityNot monotonic
2022-10-25T22:44:22.187497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64133
 
1.8%
63129
 
1.7%
60122
 
1.6%
59118
 
1.6%
72115
 
1.6%
57114
 
1.5%
62111
 
1.5%
67110
 
1.5%
68109
 
1.5%
58108
 
1.5%
Other values (238)6226
84.2%
ValueCountFrequency (%)
11
 
< 0.1%
21
 
< 0.1%
32
 
< 0.1%
51
 
< 0.1%
72
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
106
0.1%
116
0.1%
127
0.1%
ValueCountFrequency (%)
1755811
< 0.1%
1742841
< 0.1%
1738091
< 0.1%
169651
< 0.1%
169221
< 0.1%
167891
< 0.1%
136011
< 0.1%
134981
< 0.1%
134201
< 0.1%
134111
< 0.1%

two_adults_and_one_minor
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct237
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.74442191
Minimum0
Maximum72589
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:22.579077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q117
median23
Q332
95-th percentile83
Maximum72589
Range72589
Interquartile range (IQR)15

Descriptive statistics

Standard deviation1472.490111
Coefficient of variation (CV)16.97504092
Kurtosis2218.429801
Mean86.74442191
Median Absolute Deviation (MAD)7
Skewness46.03156901
Sum641475
Variance2168227.127
MonotonicityNot monotonic
2022-10-25T22:44:22.752863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20334
 
4.5%
17321
 
4.3%
22306
 
4.1%
21302
 
4.1%
18293
 
4.0%
16293
 
4.0%
19287
 
3.9%
25263
 
3.6%
23260
 
3.5%
15256
 
3.5%
Other values (227)4480
60.6%
ValueCountFrequency (%)
01
 
< 0.1%
12
 
< 0.1%
21
 
< 0.1%
38
 
0.1%
413
 
0.2%
527
 
0.4%
633
0.4%
750
0.7%
862
0.8%
979
1.1%
ValueCountFrequency (%)
725891
< 0.1%
711041
< 0.1%
701321
< 0.1%
59971
< 0.1%
58601
< 0.1%
58541
< 0.1%
58431
< 0.1%
57281
< 0.1%
56401
< 0.1%
51921
< 0.1%

two_adults_and_two_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct270
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.5643002
Minimum1
Maximum66279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:22.947438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q113
median18
Q327
95-th percentile101.3
Maximum66279
Range66278
Interquartile range (IQR)14

Descriptive statistics

Standard deviation1352.533078
Coefficient of variation (CV)16.99924557
Kurtosis2205.26432
Mean79.5643002
Median Absolute Deviation (MAD)6
Skewness45.85469424
Sum588378
Variance1829345.726
MonotonicityNot monotonic
2022-10-25T22:44:23.178646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13381
 
5.2%
14380
 
5.1%
15379
 
5.1%
16370
 
5.0%
17348
 
4.7%
12339
 
4.6%
18315
 
4.3%
11309
 
4.2%
20290
 
3.9%
19289
 
3.9%
Other values (260)3995
54.0%
ValueCountFrequency (%)
12
 
< 0.1%
23
 
< 0.1%
312
 
0.2%
429
 
0.4%
547
 
0.6%
684
 
1.1%
7137
1.9%
8183
2.5%
9225
3.0%
10269
3.6%
ValueCountFrequency (%)
662791
< 0.1%
654941
< 0.1%
643531
< 0.1%
72231
< 0.1%
71691
< 0.1%
70651
< 0.1%
57171
< 0.1%
56141
< 0.1%
54971
< 0.1%
52271
< 0.1%

two_adults_and_three_or_more_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct146
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.6474645
Minimum0
Maximum15398
Zeros327
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:23.431441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q37
95-th percentile21
Maximum15398
Range15398
Interquartile range (IQR)5

Descriptive statistics

Standard deviation317.807745
Coefficient of variation (CV)17.04294677
Kurtosis2181.901953
Mean18.6474645
Median Absolute Deviation (MAD)2
Skewness45.54969666
Sum137898
Variance101001.7628
MonotonicityNot monotonic
2022-10-25T22:44:23.641148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21046
14.1%
31028
13.9%
4904
12.2%
1812
11.0%
5677
9.2%
6485
6.6%
7386
 
5.2%
0327
 
4.4%
8309
 
4.2%
9223
 
3.0%
Other values (136)1198
16.2%
ValueCountFrequency (%)
0327
 
4.4%
1812
11.0%
21046
14.1%
31028
13.9%
4904
12.2%
5677
9.2%
6485
6.6%
7386
 
5.2%
8309
 
4.2%
9223
 
3.0%
ValueCountFrequency (%)
153981
< 0.1%
153891
< 0.1%
151791
< 0.1%
22021
< 0.1%
21941
< 0.1%
21671
< 0.1%
14181
< 0.1%
14111
< 0.1%
13511
< 0.1%
9881
< 0.1%

two_adults_over_35_and_one_adult_from_16_to_34
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct170
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.10507099
Minimum2
Maximum74432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:23.865282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13
Q120
median27
Q337
95-th percentile63
Maximum74432
Range74430
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1529.828245
Coefficient of variation (CV)16.97827024
Kurtosis2214.30501
Mean90.10507099
Median Absolute Deviation (MAD)8
Skewness45.98573939
Sum666327
Variance2340374.46
MonotonicityNot monotonic
2022-10-25T22:44:24.135570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22301
 
4.1%
25283
 
3.8%
24277
 
3.7%
27271
 
3.7%
19262
 
3.5%
26257
 
3.5%
23257
 
3.5%
20253
 
3.4%
21246
 
3.3%
18243
 
3.3%
Other values (160)4745
64.2%
ValueCountFrequency (%)
22
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
612
 
0.2%
714
 
0.2%
820
 
0.3%
928
 
0.4%
1051
0.7%
1198
1.3%
1296
1.3%
ValueCountFrequency (%)
744321
< 0.1%
739581
< 0.1%
737191
< 0.1%
64191
< 0.1%
64151
< 0.1%
63891
< 0.1%
59701
< 0.1%
58991
< 0.1%
58851
< 0.1%
58171
< 0.1%

two_adults_over_35_and_one_adult_from_16_to_34_and_one_minor
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct137
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.62393509
Minimum0
Maximum25255
Zeros36
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:24.331545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q313
95-th percentile28
Maximum25255
Range25255
Interquartile range (IQR)8

Descriptive statistics

Standard deviation520.5832115
Coefficient of variation (CV)16.99922658
Kurtosis2203.415022
Mean30.62393509
Median Absolute Deviation (MAD)3
Skewness45.83097138
Sum226464
Variance271006.8801
MonotonicityNot monotonic
2022-10-25T22:44:24.556480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6648
 
8.8%
5618
 
8.4%
7615
 
8.3%
8577
 
7.8%
4539
 
7.3%
9528
 
7.1%
10460
 
6.2%
3414
 
5.6%
11383
 
5.2%
12323
 
4.4%
Other values (127)2290
31.0%
ValueCountFrequency (%)
036
 
0.5%
1126
 
1.7%
2254
 
3.4%
3414
5.6%
4539
7.3%
5618
8.4%
6648
8.8%
7615
8.3%
8577
7.8%
9528
7.1%
ValueCountFrequency (%)
252551
< 0.1%
251881
< 0.1%
250451
< 0.1%
22521
< 0.1%
21971
< 0.1%
21471
< 0.1%
21431
< 0.1%
21301
< 0.1%
20641
< 0.1%
20171
< 0.1%

two_adults_over_35_and_one_adult_from_16_to_34_and_two_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct96
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.00689655
Minimum0
Maximum9283
Zeros590
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:24.743025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile10
Maximum9283
Range9283
Interquartile range (IQR)3

Descriptive statistics

Standard deviation187.0524042
Coefficient of variation (CV)16.99410941
Kurtosis2212.40324
Mean11.00689655
Median Absolute Deviation (MAD)2
Skewness45.93022907
Sum81396
Variance34988.60193
MonotonicityNot monotonic
2022-10-25T22:44:24.966104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21335
18.1%
11187
16.1%
31129
15.3%
4917
12.4%
5664
9.0%
0590
8.0%
6496
 
6.7%
7300
 
4.1%
8222
 
3.0%
9147
 
2.0%
Other values (86)408
 
5.5%
ValueCountFrequency (%)
0590
8.0%
11187
16.1%
21335
18.1%
31129
15.3%
4917
12.4%
5664
9.0%
6496
 
6.7%
7300
 
4.1%
8222
 
3.0%
9147
 
2.0%
ValueCountFrequency (%)
92831
< 0.1%
90571
< 0.1%
87921
< 0.1%
8311
< 0.1%
8131
< 0.1%
7851
< 0.1%
7571
< 0.1%
7451
< 0.1%
7441
< 0.1%
7341
< 0.1%

three_adults_and_0_or_more_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct158
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.9281947
Minimum4
Maximum106779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:25.211015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile24
Q133
median41
Q351
95-th percentile70
Maximum106779
Range106775
Interquartile range (IQR)18

Descriptive statistics

Standard deviation2153.934463
Coefficient of variation (CV)16.96970849
Kurtosis2223.168613
Mean126.9281947
Median Absolute Deviation (MAD)9
Skewness46.08926491
Sum938634
Variance4639433.672
MonotonicityNot monotonic
2022-10-25T22:44:25.456144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35238
 
3.2%
40230
 
3.1%
37229
 
3.1%
34222
 
3.0%
36216
 
2.9%
39215
 
2.9%
38214
 
2.9%
44213
 
2.9%
42208
 
2.8%
32203
 
2.7%
Other values (148)5207
70.4%
ValueCountFrequency (%)
42
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
113
 
< 0.1%
127
0.1%
135
0.1%
148
0.1%
157
0.1%
ValueCountFrequency (%)
1067791
< 0.1%
1040791
< 0.1%
1020201
< 0.1%
82301
< 0.1%
81241
< 0.1%
81041
< 0.1%
80131
< 0.1%
78881
< 0.1%
77951
< 0.1%
77001
< 0.1%

two_adults_over_35_and_two_adults_from_16_to_34
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct172
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.52981744
Minimum0
Maximum51993
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:25.656244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median17
Q325
95-th percentile56
Maximum51993
Range51993
Interquartile range (IQR)13

Descriptive statistics

Standard deviation1061.293287
Coefficient of variation (CV)16.97259533
Kurtosis2218.369963
Mean62.52981744
Median Absolute Deviation (MAD)6
Skewness46.03605095
Sum462408
Variance1126343.442
MonotonicityNot monotonic
2022-10-25T22:44:25.844365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16383
 
5.2%
13382
 
5.2%
12378
 
5.1%
14373
 
5.0%
15336
 
4.5%
11331
 
4.5%
10315
 
4.3%
17305
 
4.1%
18303
 
4.1%
9295
 
4.0%
Other values (162)3994
54.0%
ValueCountFrequency (%)
01
 
< 0.1%
15
 
0.1%
216
 
0.2%
329
 
0.4%
450
 
0.7%
589
 
1.2%
6132
1.8%
7177
2.4%
8269
3.6%
9295
4.0%
ValueCountFrequency (%)
519931
< 0.1%
514191
< 0.1%
507241
< 0.1%
41151
< 0.1%
41071
< 0.1%
40421
< 0.1%
39841
< 0.1%
39691
< 0.1%
39101
< 0.1%
37242
< 0.1%

two_adults_over_35_and_two_adults_from_16_to_34_and_one_minor
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct90
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.713184584
Minimum0
Maximum8131
Zeros637
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:26.117836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum8131
Range8131
Interquartile range (IQR)4

Descriptive statistics

Standard deviation165.0029789
Coefficient of variation (CV)16.98752633
Kurtosis2213.092108
Mean9.713184584
Median Absolute Deviation (MAD)2
Skewness45.95178104
Sum71829
Variance27225.98305
MonotonicityNot monotonic
2022-10-25T22:44:26.289845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21383
18.7%
11310
17.7%
31245
16.8%
4916
12.4%
5671
9.1%
0637
8.6%
6422
 
5.7%
7267
 
3.6%
8196
 
2.7%
990
 
1.2%
Other values (80)258
 
3.5%
ValueCountFrequency (%)
0637
8.6%
11310
17.7%
21383
18.7%
31245
16.8%
4916
12.4%
5671
9.1%
6422
 
5.7%
7267
 
3.6%
8196
 
2.7%
990
 
1.2%
ValueCountFrequency (%)
81311
< 0.1%
80091
< 0.1%
78031
< 0.1%
7131
< 0.1%
7051
< 0.1%
6791
< 0.1%
6611
< 0.1%
6461
< 0.1%
6431
< 0.1%
6071
< 0.1%

two_adults_over_35_and_two_adults_from_16_to_34_and_two_or_more_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct74
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.193103448
Minimum0
Maximum3537
Zeros2488
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:26.650139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum3537
Range3537
Interquartile range (IQR)2

Descriptive statistics

Standard deviation71.49073442
Coefficient of variation (CV)17.04959949
Kurtosis2183.359492
Mean4.193103448
Median Absolute Deviation (MAD)1
Skewness45.53116149
Sum31008
Variance5110.925108
MonotonicityNot monotonic
2022-10-25T22:44:26.828779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02488
33.6%
12179
29.5%
21292
17.5%
3652
 
8.8%
4372
 
5.0%
5170
 
2.3%
678
 
1.1%
749
 
0.7%
822
 
0.3%
914
 
0.2%
Other values (64)79
 
1.1%
ValueCountFrequency (%)
02488
33.6%
12179
29.5%
21292
17.5%
3652
 
8.8%
4372
 
5.0%
5170
 
2.3%
678
 
1.1%
749
 
0.7%
822
 
0.3%
914
 
0.2%
ValueCountFrequency (%)
35371
< 0.1%
34381
< 0.1%
33611
< 0.1%
3861
< 0.1%
3841
< 0.1%
3771
< 0.1%
3681
< 0.1%
3521
< 0.1%
3391
< 0.1%
3011
< 0.1%

four_adults_and_0_or_more_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct118
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.368357
Minimum1
Maximum48740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:27.105403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q114
median18
Q324
95-th percentile35
Maximum48740
Range48739
Interquartile range (IQR)10

Descriptive statistics

Standard deviation974.0107017
Coefficient of variation (CV)16.97818715
Kurtosis2223.464047
Mean57.368357
Median Absolute Deviation (MAD)5
Skewness46.0736974
Sum424239
Variance948696.847
MonotonicityNot monotonic
2022-10-25T22:44:27.321833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15430
 
5.8%
18407
 
5.5%
14406
 
5.5%
17401
 
5.4%
16389
 
5.3%
13380
 
5.1%
20366
 
4.9%
19360
 
4.9%
21327
 
4.4%
12324
 
4.4%
Other values (108)3605
48.7%
ValueCountFrequency (%)
12
 
< 0.1%
26
 
0.1%
314
 
0.2%
418
 
0.2%
529
 
0.4%
664
 
0.9%
790
 
1.2%
8137
1.9%
9168
2.3%
10233
3.2%
ValueCountFrequency (%)
487401
< 0.1%
469771
< 0.1%
456961
< 0.1%
39441
< 0.1%
38931
< 0.1%
37531
< 0.1%
37121
< 0.1%
36511
< 0.1%
36491
< 0.1%
35301
< 0.1%

five_adults_and_0_or_more_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct160
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.43245436
Minimum1
Maximum59406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:27.546602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q113
median20
Q328
95-th percentile49
Maximum59406
Range59405
Interquartile range (IQR)15

Descriptive statistics

Standard deviation1131.437694
Coefficient of variation (CV)17.03139986
Kurtosis2247.432423
Mean66.43245436
Median Absolute Deviation (MAD)7
Skewness46.19403382
Sum491268
Variance1280151.255
MonotonicityNot monotonic
2022-10-25T22:44:27.836604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18326
 
4.4%
14309
 
4.2%
17309
 
4.2%
13300
 
4.1%
16294
 
4.0%
11287
 
3.9%
15285
 
3.9%
19284
 
3.8%
12279
 
3.8%
20259
 
3.5%
Other values (150)4463
60.4%
ValueCountFrequency (%)
12
 
< 0.1%
23
 
< 0.1%
322
 
0.3%
441
 
0.6%
581
 
1.1%
677
 
1.0%
7127
1.7%
8189
2.6%
9200
2.7%
10258
3.5%
ValueCountFrequency (%)
594061
< 0.1%
538871
< 0.1%
504631
< 0.1%
58381
< 0.1%
51841
< 0.1%
50791
< 0.1%
45961
< 0.1%
44851
< 0.1%
44321
< 0.1%
41431
< 0.1%

fifteen_or_more_inhabitants
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct46
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6843813387
Minimum0
Maximum622
Zeros6141
Zeros (%)83.0%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:28.043076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum622
Range622
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.71054137
Coefficient of variation (CV)17.11113485
Kurtosis2230.954498
Mean0.6843813387
Median Absolute Deviation (MAD)0
Skewness45.83761583
Sum5061
Variance137.1367793
MonotonicityNot monotonic
2022-10-25T22:44:28.237249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
06141
83.0%
1895
 
12.1%
2173
 
2.3%
377
 
1.0%
425
 
0.3%
510
 
0.1%
76
 
0.1%
65
 
0.1%
94
 
0.1%
323
 
< 0.1%
Other values (36)56
 
0.8%
ValueCountFrequency (%)
06141
83.0%
1895
 
12.1%
2173
 
2.3%
377
 
1.0%
425
 
0.3%
510
 
0.1%
65
 
0.1%
76
 
0.1%
83
 
< 0.1%
94
 
0.1%
ValueCountFrequency (%)
6221
 
< 0.1%
5521
 
< 0.1%
5131
 
< 0.1%
551
 
< 0.1%
521
 
< 0.1%
501
 
< 0.1%
491
 
< 0.1%
483
< 0.1%
472
< 0.1%
462
< 0.1%

only_minors
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct37
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4742393509
Minimum0
Maximum422
Zeros6310
Zeros (%)85.3%
Negative0
Negative (%)0.0%
Memory size57.9 KiB
2022-10-25T22:44:28.431878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum422
Range422
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.104903248
Coefficient of variation (CV)17.09032208
Kurtosis2254.615502
Mean0.4742393509
Median Absolute Deviation (MAD)0
Skewness46.13931176
Sum3507
Variance65.68945667
MonotonicityNot monotonic
2022-10-25T22:44:28.616787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
06310
85.3%
1888
 
12.0%
2118
 
1.6%
312
 
0.2%
188
 
0.1%
174
 
0.1%
194
 
0.1%
44
 
0.1%
254
 
0.1%
333
 
< 0.1%
Other values (27)40
 
0.5%
ValueCountFrequency (%)
06310
85.3%
1888
 
12.0%
2118
 
1.6%
312
 
0.2%
44
 
0.1%
52
 
< 0.1%
63
 
< 0.1%
72
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
4221
 
< 0.1%
4051
 
< 0.1%
3421
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
341
 
< 0.1%
333
< 0.1%
292
< 0.1%
282
< 0.1%
273
< 0.1%

Year
Categorical

UNIFORM

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
2020
2465 
2018
2465 
2019
2465 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters29580
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
20202465
33.3%
20182465
33.3%
20192465
33.3%

Length

2022-10-25T22:44:28.776657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T22:44:28.944032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
20202465
33.3%
20182465
33.3%
20192465
33.3%

Most occurring characters

ValueCountFrequency (%)
29860
33.3%
09860
33.3%
14930
16.7%
82465
 
8.3%
92465
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29580
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
29860
33.3%
09860
33.3%
14930
16.7%
82465
 
8.3%
92465
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common29580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
29860
33.3%
09860
33.3%
14930
16.7%
82465
 
8.3%
92465
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII29580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29860
33.3%
09860
33.3%
14930
16.7%
82465
 
8.3%
92465
 
8.3%

Interactions

2022-10-25T22:44:10.911121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:18.701549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:23.927559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:28.825977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:33.907607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:39.171423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:43.920063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:49.172911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:54.353796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:59.816717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:04.770220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:09.026173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:13.930704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:20.039207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:25.086908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:29.795855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:34.440748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:39.359028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:44.236972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:49.335037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:54.463157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:00.323214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:05.485587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:11.114298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:18.971113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:24.133642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:29.028200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:34.192837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:39.357122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:44.266067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:49.401178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:54.620683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:00.040448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:04.940862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:09.205381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:14.191895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:20.237500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:25.309392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:30.128104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:34.672416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:39.558457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:44.423529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:49.516694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:54.678555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:00.561586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:05.655233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:11.348583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:19.164976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:24.330586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:29.258648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:34.411611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:39.537792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:44.447377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:49.611577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:54.870600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:00.293067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:05.129638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:09.411130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:14.501223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:20.414969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:25.585844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:30.373941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:34.890238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:39.757825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:44.661449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:49.693826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:54.940889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:00.765894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:05.931859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:11.512466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:19.358607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:24.562134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:29.478364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:34.606507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:39.751699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-25T22:43:33.791690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:38.703692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:43.617903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:48.543499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:53.822319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:59.646904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:04.821600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:10.041215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:15.530704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:23.491530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:28.478428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:33.514017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:38.740823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:43.567195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:48.728178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:53.913106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:59.461511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:04.252893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:08.680678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:13.423147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:19.498303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:24.672079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:29.410943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:34.032481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:38.989505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:43.823395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:48.743107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:54.044622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:59.834274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:05.035584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:10.448437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:15.689204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:23.729134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:28.655310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:33.721943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:39.010688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:43.757821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:48.956172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:54.164438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:42:59.646188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:04.593895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:08.858438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:13.693696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:19.759721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:24.872455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:29.614002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:34.194337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:39.163848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:44.052289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:48.970202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:43:54.251273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:00.097080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:05.269554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T22:44:10.682041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-25T22:44:29.139425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-25T22:44:29.595941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-25T22:44:30.096731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-25T22:44:30.557709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-25T22:44:30.963729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-25T22:44:16.048072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-25T22:44:17.411649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

sectiontotalsingle_women_aged_16_to_64single_men_aged_16_to_64single_women_aged_65_or_oversingle_men_aged_65_or_overadult_women_with_one_or_more_minorsadult_men_with_one_or_more_minorstwo_adults_from_16_to_64_and_without_minorstwo_adults_one_at_least_65_and_without_minorstwo_adults_and_one_minortwo_adults_and_two_minorstwo_adults_and_three_or_more_minorstwo_adults_over_35_and_one_adult_from_16_to_34two_adults_over_35_and_one_adult_from_16_to_34_and_one_minortwo_adults_over_35_and_one_adult_from_16_to_34_and_two_minorsthree_adults_and_0_or_more_minorstwo_adults_over_35_and_two_adults_from_16_to_34two_adults_over_35_and_two_adults_from_16_to_34_and_one_minortwo_adults_over_35_and_two_adults_from_16_to_34_and_two_or_more_minorsfour_adults_and_0_or_more_minorsfive_adults_and_0_or_more_minorsfifteen_or_more_inhabitantsonly_minorsYear
0Ciudad de Madrid1307682.0117998.0116863.0126695.037538.026232.05328.0163183.0175581.070132.064353.015179.074432.025255.09283.0106779.051993.08131.03537.048740.059406.0622.0422.02020
101. Centro69187.011695.013596.05057.02269.0815.0172.013563.05267.02113.01249.0241.02247.0476.0213.04719.01123.0181.067.01898.02153.048.025.02020
21001.0568.075.093.052.017.06.05.097.074.014.017.01.022.03.02.036.011.03.00.020.020.00.00.02020
31002.0492.0111.099.032.017.04.01.096.030.09.04.00.019.01.00.033.06.01.00.015.014.00.00.02020
41003.0862.0137.0164.068.043.07.02.0148.080.018.09.05.030.010.03.050.016.07.00.028.037.00.00.02020
51004.0600.0103.0100.055.023.04.00.077.071.020.09.01.024.08.03.048.012.02.01.018.019.02.00.02020
61006.0888.0123.0174.082.047.012.01.0181.075.023.018.02.025.010.03.057.014.02.01.015.023.00.00.02020
71007.0426.064.076.047.019.05.02.066.033.016.010.04.012.05.02.035.07.01.00.011.010.00.01.02020
81008.0411.051.064.043.019.07.01.082.040.012.014.01.08.02.01.033.07.01.01.013.011.00.00.02020
91009.0716.0114.0125.064.035.015.00.0139.058.029.013.02.022.07.01.049.015.02.00.014.012.00.00.02020

Last rows

sectiontotalsingle_women_aged_16_to_64single_men_aged_16_to_64single_women_aged_65_or_oversingle_men_aged_65_or_overadult_women_with_one_or_more_minorsadult_men_with_one_or_more_minorstwo_adults_from_16_to_64_and_without_minorstwo_adults_one_at_least_65_and_without_minorstwo_adults_and_one_minortwo_adults_and_two_minorstwo_adults_and_three_or_more_minorstwo_adults_over_35_and_one_adult_from_16_to_34two_adults_over_35_and_one_adult_from_16_to_34_and_one_minortwo_adults_over_35_and_one_adult_from_16_to_34_and_two_minorsthree_adults_and_0_or_more_minorstwo_adults_over_35_and_two_adults_from_16_to_34two_adults_over_35_and_two_adults_from_16_to_34_and_one_minortwo_adults_over_35_and_two_adults_from_16_to_34_and_two_or_more_minorsfour_adults_and_0_or_more_minorsfive_adults_and_0_or_more_minorsfifteen_or_more_inhabitantsonly_minorsYear
738521023.0447.042.037.041.015.016.02.068.045.029.031.04.026.010.02.034.016.02.02.011.014.00.00.02019
738621025.0484.018.020.041.010.010.02.052.089.022.040.014.032.011.02.038.025.03.01.024.029.01.00.02019
738721026.0631.071.049.042.014.017.04.090.062.037.023.03.057.014.04.051.042.03.00.027.021.00.00.02019
738821027.0912.067.094.016.010.048.014.0117.041.0105.0140.025.035.041.013.048.055.010.02.018.013.00.00.02019
738921028.0521.044.029.014.07.014.04.057.046.039.059.018.038.018.08.035.044.05.02.09.029.00.02.02019
739021029.0526.035.033.07.05.021.09.036.024.058.087.026.030.033.011.025.051.08.02.09.016.00.00.02019
739121030.0563.033.059.026.08.016.04.070.049.049.061.011.038.019.03.036.043.05.00.012.021.00.00.02019
739221031.0948.099.094.014.07.041.08.0155.035.0164.0153.020.029.023.06.029.025.06.00.017.023.00.00.02019
739321032.0658.072.067.018.06.033.013.094.025.062.0128.023.022.018.03.022.027.07.00.06.09.01.02.02019
739421033.0907.0129.0136.09.05.086.015.0123.018.0132.0105.028.018.011.06.043.012.02.01.011.013.03.01.02019